Systems and methods for predicting location, onset, and/or change of coronary lesions

ABSTRACT

Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual&#39;s vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient&#39;s vascular system; and executing the machine learning algorithm on the patient&#39;s geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient&#39;s geometric model.

RELATED APPLICATION(S)

This application is a continuation of and claims the benefit of priorityto U.S. patent application Ser. No. 14/011,151, filed Aug. 27, 2013,which is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

Various embodiments of the present disclosure relate generally tomedical imaging and related methods. More specifically, particularembodiments of the present disclosure relate to systems and methods forpredicting the location, onset, and/or change of coronary lesions fromfactors such as vessel geometry, physiology, and hemodynamics.

BACKGROUND

Coronary artery disease (“CAD”) may produce coronary lesions, such as astenosis (abnormal narrowing of a blood vessel), in the blood vesselsproviding blood to the heart. As a result, blood flow to the heart maybe restricted. A patient suffering from coronary artery disease mayexperience chest pain, referred to as “chronic stable angina” duringphysical exertion, or “unstable angina” when the patient is at rest. Amore severe manifestation of disease may lead to myocardial infarction,or heart attack.

A need exists to provide more accurate data relating to coronarylesions, e.g., size, shape, location, functional significance (e.g.,whether the lesion impacts blood flow), etc. Patients suffering fromchest pain and/or exhibiting symptoms of coronary artery disease may besubjected to one or more tests that may provide some indirect evidencerelating to coronary lesions. For example, noninvasive tests may includeelectrocardiograms, biomarker evaluation from blood tests, treadmilltests, echocardiography, single positron emission computed tomography(SPECT), positron emission tomography (PET), and coronary computedtomographic angiography (CCTA). The noninvasive tests may provideindirect evidence of coronary lesions by looking for changes inelectrical activity of the heart (e.g., using electrocardiography(ECG)), motion of the myocardium (e.g., using stress echocardiography),perfusion of the myocardium (e.g., using PET or SPECT), or metabolicchanges (e.g., using biomarkers). However, these noninvasive teststypically do not provide a direct assessment of coronary lesions orassess blood flow rates. Thus, patients may also require an invasivetest, such as diagnostic cardiac catheterization, to visualize coronarylesions. Diagnostic cardiac catheterization may include performingconventional coronary angiography (CCA) to gather anatomic data oncoronary lesions by providing a doctor with an image of the size andshape of the arteries.

However, both invasive and noninvasive tests for CAD are only useful indetermining an amount of disease and/or risk of heart attack that hasalready been incurred. That is, tests for CAD are unable to predictfuture amounts of plaque build-up, stenosis, or other CAD that is likelyto occur based on other known characteristics of an individual. Eventhough CAD is known to be associated with various risk factors,including smoking, diabetes, hypertension, and dietary habits, notechniques exist for predicting the onset of CAD. In addition, notechniques exist for predicting the type or location of plaque that islikely to develop in view of other known characteristics of anindividual.

Consequently, the present disclosure describes new approaches forpredicting the location, onset, and/or change of coronary lesions fromfactors such as vessel geometry, physiology, and hemodynamics.

SUMMARY

Systems and methods are disclosed for predicting the location, onset,and/or change of coronary lesions from factors such as vessel geometry,physiology, and hemodynamics.

According to one embodiment, a method is disclosed for predictinginformation relating to a coronary lesion. The method includes:acquiring, for each of a plurality of individuals, a geometric model,blood flow characteristics, and plaque information for at least part ofthe individual's vascular system; identifying, for each of a pluralityof points in the geometric models, features predictive of the presenceof plaque within the geometric models and blood flow characteristics ofthe plurality of individuals; training a machine learning algorithmbased on the geometric models and blood flow characteristics for each ofthe plurality of individuals, and the predictive features; acquiring,for a patient, a geometric model and blood flow characteristics for atleast part of the patient's vascular system; and executing the machinelearning algorithm on the patient's geometric model and blood flowcharacteristics to determine, based on the predictive features, plaqueinformation of the patient for at least one point in the patient'sgeometric model.

According to another embodiment, a system is disclosed for predictinginformation relating to a coronary lesion. The system includes a datastorage device storing instructions for predicting information relatingto a coronary lesion; and a processor configured to execute theinstructions to perform a method including the steps of: acquiring, foreach of a plurality of individuals, a geometric model, blood flowcharacteristics, and plaque information for at least part of theindividual's vascular system; identifying, for each of a plurality ofpoints in the geometric models, features predictive of the presence ofplaque within the geometric models and blood flow characteristics of theplurality of individuals; training a machine learning algorithm based onthe geometric models and blood flow characteristics for each of theplurality of individuals, and the predictive features; acquiring, for apatient, a geometric model and blood flow characteristics for at leastpart of the patient's vascular system; and executing the machinelearning algorithm on the patient's geometric model and blood flowcharacteristics to determine, based on the predictive features, plaqueinformation of the patient for at least one point in the patient'sgeometric model.

According to another embodiment, a non-transitory computer-readablemedium is disclosed storing instructions that, when executed by acomputer, cause the computer to perform a method for predictinginformation relating to a coronary lesion, the method including:acquiring, for each of a plurality of individuals, a geometric model,blood flow characteristics, and plaque information for at least part ofthe individual's vascular system; identifying, for each of a pluralityof points in the geometric models, features predictive of the presenceof plaque within the geometric models and blood flow characteristics ofthe plurality of individuals; training a machine learning algorithmbased on the geometric models and blood flow characteristics for each ofthe plurality of individuals, and the predictive features; acquiring,for a patient, a geometric model and blood flow characteristics for atleast part of the patient's vascular system; and executing the machinelearning algorithm on the patient's geometric model and blood flowcharacteristics to determine, based on the predictive features, plaqueinformation of the patient for at least one point in the patient'sgeometric model.

According to another embodiment, a computer-implemented method isdisclosed for predicting information relating to a coronary lesion. Onemethod includes acquiring, over a network, for a patient, a geometricmodel and blood flow characteristics for at least part of the patient'svascular system; and determining plaque information of the patient forat least one point in the patient's geometric model by executing on thepatient's geometric model and blood flow characteristics, a machinelearning algorithm trained based on plaque predictive features derivedfrom geometric models, blood flow characteristics, and plaqueinformation obtained for each of a plurality of individuals.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network forpredicting the location, onset, and/or change of coronary lesions fromfactors such as vessel geometry, physiology, and hemodynamics, accordingto an exemplary embodiment of the present disclosure.

FIG. 2 is a diagram of an exemplary three-dimensional mesh of ageometric model used in predicting the location, onset, and/or change ofcoronary lesions from factors such as vessel geometry, physiology, andhemodynamics, according to an exemplary embodiment of the presentdisclosure.

FIG. 3A is a block diagram of an exemplary method of training a machinelearning system for predicting the location, onset, and/or change ofcoronary lesions from factors such as vessel geometry, physiology, andhemodynamics s, according to an exemplary embodiment of the presentdisclosure.

FIG. 3B is a block diagram of an exemplary method of using a trainedmachine learning system for predicting the location, onset, and/orchange of coronary lesions from factors such as vessel geometry,physiology, and hemodynamics, according to an exemplary embodiment ofthe present disclosure.

FIG. 4A is a block diagram of an exemplary method of training a machinelearning system for predicting the location of coronary lesions fromfactors such as vessel geometry, physiology, and hemodynamics, accordingto an exemplary embodiment of the present disclosure.

FIG. 4B is a block diagram of an exemplary method of using a trainedmachine learning system for predicting the location of coronary lesionsfrom factors such as vessel geometry, physiology, and hemodynamics,according to an exemplary embodiment of the present disclosure.

FIG. 5A is a block diagram of an exemplary method of training a machinelearning system for predicting the onset and/or change (e.g., rate ofgrowth/shrinkage) of coronary lesions from vessel geometry, physiology,and hemodynamics, according to an exemplary embodiment of the presentdisclosure.

FIG. 5B is a block diagram of an exemplary method of using a trainedmachine learning system for predicting the onset and/or change (e.g.,rate of growth/shrinkage) of coronary lesions from vessel geometry,physiology, and hemodynamics, according to an exemplary embodiment ofthe present disclosure.

FIG. 6 is a simplified block diagram of an exemplary computer system inwhich embodiments of the present disclosure may be implemented.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

The present disclosure describes an approach for providing prognosis ofcoronary artery disease (“CAD”) and for predicting plaquegrowth/shrinkage based on patient-specific geometry and blood flowcharacteristics. Specifically, the present disclosure describes a systemthat receives patient information (e.g., 3D cardiac imaging, patientdemographics, and history) and provides a patient-specific andlocation-specific risk score for the pathogenesis of CAD. Although thepresent disclosure is described with particular reference to coronaryartery disease, the same systems and methods are applicable to creatinga patient-specific prediction of lesion formation in other vascularsystems beyond the coronary arteries.

More specifically, the present disclosure describes certain principlesand embodiments for using patients' cardiac imaging to: (1) derive apatient-specific geometric model of the coronary vessels; and (2)perform coronary flow simulation to extract hemodynamic characteristics,patient physiological information, and boundary conditions in order topredict the onset and location of coronary lesions. The presentdisclosure is not limited to a physics-based simulation of blood flow topredict the locations predisposed to plaque formation, but rather usesmachine learning to predict the lesion location by incorporating variousrisk factors, including patient demographics and coronary geometry, aswell as the results of patient-specific biophysical simulations (e.g.,hemodynamic characteristics). If additional diagnostic test results areavailable, those results may also be used in the training andprediction. According to certain embodiments, the presently disclosedmethods involve two phases: (1) a training phase in which the machinelearning system is trained to predict one or more locations of coronarylesions, and (2) a production phase in which the machine learning systemis used to produce one or more locations of coronary lesions.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system and network for predicting the location, onset, and/orchange of coronary lesions from vessel geometry, physiology, andhemodynamics. Specifically, FIG. 1 depicts a plurality of physiciandevices or systems 102 and third party provider devices or systems 104,any of which may be connected to an electronic network 101, such as theInternet, through one or more computers, servers, and/or handheld mobiledevices. Physicians and/or third party providers associated withphysician devices or systems 102 and/or third party provider devices orsystems 104, respectively, may create or otherwise obtain images of oneor more patients' cardiac and/or vascular systems. The physicians and/orthird party providers may also obtain any combination ofpatient-specific information, such as age, medical history, bloodpressure, blood viscosity, etc. Physicians and/or third party providersmay transmit the cardiac/vascular images and/or patient-specificinformation to server systems 106 over the electronic network 101.Server systems 106 may include storage devices for storing images anddata received from physician devices or systems 102 and/or third partyprovider devices or systems 104. Server systems 106 may also includeprocessing devices for processing images and data stored in the storagedevices.

FIG. 2 is a diagram of an exemplary three-dimensional mesh of ageometric model 200 used in predicting the location, onset, and/orchange of coronary lesions from vessel geometry, according to anexemplary embodiment of the present disclosure. For example, asdescribed above, a third party provider or physician may obtainpatient-specific anatomical data of one or more patients.Patient-specific anatomical data may include data regarding the geometryof the patient's heart, e.g., at least a portion of the patient's aorta,a proximal portion of the main coronary arteries (and the branchesextending therefrom) connected to the aorta, and the myocardium.However, as-described above, patient-specific anatomical data may alsoor alternatively be obtained in relation to any portion of the patient'svasculature, including beyond the patient's heart.

Initially, a patient may be selected, e.g., when the physiciandetermines that information about the patient's coronary blood flow isdesired, e.g., if the patient is experiencing symptoms associated withcoronary artery disease, such as chest pain, heart attack, etc. Thepatient-specific anatomical data may be obtained noninvasively, e.g.,using a noninvasive imaging method. For example, CCTA is an imagingmethod in which a user may operate a computer tomography (CT) scanner toview and create images of structures, e.g., the myocardium, the aorta,the main coronary arteries, and other blood vessels connected thereto.The CCTA data may be time-varying, e.g., to show changes in vessel shapeover a cardiac cycle. CCTA may be used to produce an image of thepatient's heart. For example, 64-slice CCTA data may be obtained, e.g.,data relating to 64 slices of the patient's heart, and assembled into athree-dimensional image.

Alternatively, other noninvasive imaging methods, such as magneticresonance imaging (MRI) or ultrasound (US), or invasive imaging methods,such as digital subtraction angiography (DSA), may be used to produceimages of the structures of the patient's anatomy. The imaging methodsmay involve injecting the patient intravenously with a contrast agent toenable identification of the structures of the anatomy. The resultingimaging data (e.g., provided by CCTA, MRI, etc.) may be provided by athird-party vendor, such as a radiology lab or a cardiologist, by thepatient's physician, etc.

Other patient-specific anatomical data may also be determined from thepatient noninvasively. For example, physiological data such as thepatient's blood pressure, baseline heart rate, height, weight,hematocrit, stroke volume, etc., may be measured. The blood pressure maybe the blood pressure in the patient's brachial artery (e.g., using apressure cuff), such as the maximum (systolic) and minimum (diastolic)pressures.

The patient-specific anatomical data obtained as described above may betransferred over a secure communication line (e.g., via electronicnetwork 101 of FIG. 1). For example, the data may be transferred toserver systems 106 or other computer system for performing computationalanalysis, e.g., the computational analysis described below with respectto FIGS. 3-5B. In one exemplary embodiment, the patient-specificanatomical data may be transferred to server systems 106 or othercomputer system operated by a service provider providing a web-basedservice. Alternatively, the data may be transferred to a computer systemoperated by the patient's physician or other user.

In one embodiment, server systems 106 may generate a three-dimensionalsolid model and/or three-dimensional mesh 200 based on the receivedpatient-specific anatomical data. For example, server systems 106 maygenerate the three-dimensional model and/or mesh based on any of thetechniques described in U.S. Pat. No. 8,315,812 by Taylor et al., whichissued on Nov. 20, 2012, the entirety of which is hereby incorporatedherein by reference.

FIG. 3A is a block diagram of an exemplary method 300 for training amachine learning system, based on a plurality of patients' blood flowcharacteristics and geometry, for predicting the location, onset, and/orchange of coronary lesions from vessel geometry, physiology, andhemodynamics, according to an exemplary embodiment of the presentdisclosure. Specifically, as shown in FIG. 3A, method 300 may includeobtaining patient imaging data (e.g., a geometric model) and physiologicand/or hemodynamic information 302 for a plurality of patients. Method300 may include generating feature vectors 304 based on the plurality ofpatients' imaging and physiologic and/or hemodynamic information. Method300 further includes obtaining information about plaque 306 for theplurality of patients, and formatting the information about theplurality of patients' plaque into the format that is desired of theoutput 308 of the learning system. Method 300 completes the trainingmode by inputting into a learning system 310 both the feature vectors304 formed from the plurality of patients' imaging data and physiologicand/or hemodynamic information, and the output 308 of the informationabout plaque for the plurality of patients. For example, as will bedescribed in more detail below, any suitable type of machine learningsystem may process both the feature vectors 304 and outputs 308 toidentify patterns and conclusions from that data, for later use inproducing outputs of information about a particular user's plaque.

FIG. 3B is a block diagram of an exemplary method 350 for using thetrained machine learning system 310 for predicting, for a particularpatient, the location, onset, and/or change of coronary lesions fromvessel geometry, physiology, and hemodynamics, according to an exemplaryembodiment of the present disclosure. As shown in FIG. 3B, method 350may include obtaining patient imaging data (e.g., a geometric model) andphysiologic and/or hemodynamic information 312 for a particular patient,for whom it is desired to predict plaque location, onset, and/or changebased on the trained learning system 310. Of course, method 350 mayinclude obtaining the patient imaging data and physiologic and/orhemodynamic information for any number of patients for whom it isdesired to predict plaque location, onset, and/or change based on thetrained learning system. Method 350 may include generating a featurevector 314 for each of a plurality of points of the patient's geometricmodel, based on one or more elements of the received physiologic and/orhemodynamic information. Method 350 may then include operating themachine learning system 310 on the feature vectors generated for thepatient to obtain an output 316 of the estimates of the presence oronset of plaque at each of a plurality of points in the patient'sgeometric model, and translating the output into useable information 318about the location, onset, and/or change of plaque in the patient 318.

Described below are exemplary embodiments for implementing a trainingmode method 300 and a production mode method 350 of machine learning forpredicting the location, onset, and/or change of coronary lesions fromvessel geometry, physiology, and hemodynamics, e.g. using server systems106, based on images and data received from physicians and/or thirdparty providers over electronic network 101. Specifically, the methodsof FIGS. 4A-5B may be performed by server systems 106, based oninformation received from physician devices or systems 102 and/or thirdparty provider devices or systems 104 over electronic network 101.

FIG. 4A is a block diagram of an exemplary method 400 for training amachine learning system (e.g., a machine learning system 310 executed onserver systems 106) for predicting the location of coronary lesions fromvessel geometry, physiology, and hemodynamics, according to an exemplaryembodiment of the present disclosure. Specifically, method 400 mayinclude, for one or more patients (step 402), obtaining apatient-specific geometric model of a portion of the patient'svasculature (step 404), obtaining one or more estimates of physiologicalor phenotypic parameters of the patient (step 406), and obtaining one ormore estimates of biophysical hemodynamic characteristics of the patient(step 408).

For example, the step of obtaining a patient-specific geometric model ofa portion of the patient's vasculature (step 404) may include obtaininga patient-specific model of the geometry for one or more of thepatient's blood vessels, myocardium, aorta, valves, plaques, and/orchambers. In one embodiment, this geometry may be represented as a listof points in space (possibly with a list of neighbors for each point) inwhich the space can be mapped to spatial units between points (e.g.,millimeters). In one embodiment, this model may be derived by performinga cardiac CT imaging of the patient in the end diastole phase of thecardiac cycle. This image then may be segmented manually orautomatically to identify voxels belonging to the aorta and the lumen ofthe coronary arteries. Given a 3D image of coronary vasculature, any ofthe many available methods may be used for extracting a patient-specificmodel of cardiovascular geometry. Inaccuracies in the geometry extractedautomatically may be corrected by a human observer who compares theextracted geometry with the images and makes corrections as needed. Oncethe voxels are identified, the geometric model can be derived (e.g.,using marching cubes).

The step of obtaining one or more estimates of physiological orphenotypic parameters of the patient (step 406) may include obtaining alist of one or more estimates of physiological or phenotypic parametersof the patient, such as blood pressure, blood viscosity, in vitro bloodtest results (e.g., LDL/Triglyceride cholesterol level), patient age,patient gender, the mass of the supplied tissue, etc. These parametersmay be global (e.g., blood pressure) or local (e.g., estimated densityof the vessel wall at a location). In one embodiment, the physiologicalor phenotypic parameters may include, blood pressure, hematocrit level,patient age, patient gender, myocardial mass (e.g., derived bysegmenting the myocardium in the image, and calculating the volume inthe image and using an estimated density of 1.05 g/mL to estimate themyocardial mass), general risk factors of coronary artery disease (e.g.,smoking, diabetes, hypertension, abdominal obesity, dietary habits,family history, etc.), and/or in vitro blood test results (e.g., LDL,Triglyceride cholesterol level).

The step of obtaining one or more estimates of biophysical hemodynamiccharacteristics of the patient (step 408) may include obtaining a listof one or more estimates of biophysical hemodynamic characteristics fromcomputational fluid dynamics analysis, such as wall-shear stress,oscillatory shear index, particle residence time, Reynolds number,Womersley number, local flow rate, and turbulent kinetic energy, etc.Specifically, the mean wall-shear stress, may be defined as

${{{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{\overset{arrow}{t_{s}}{\mathbb{d}t}}}}} \cdot \overset{arrow}{t_{s}}},$which may be the wall shear stress vector defined as the in-planecomponent of the surface traction vector. The oscillatory shear index(OSI), may be defined as

${\frac{1}{2}( {1 - \frac{{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{\overset{arrow}{t_{s}}{\mathbb{d}t}}}}}{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{{\overset{arrow}{t_{s}}}{\mathbb{d}t}}}}} )},$which may be a measure of the uni-directionality of shear stress. Theparticle residence time may be a measure of the time it takes blood tobe flushed from a specified fluid domain. The turbulent kinetic energy(“TKE”) may be a measure of the intensity of turbulence associated witheddies in turbulent flow, and may be characterized by measuredroot-mean-square velocity fluctuation, and may be normalized by kineticenergy. The Reynolds number may be defined as

$\frac{\rho\;{UD}}{\mu}$where (ρ: density of blood, U: average flow velocity, D: vesseldiameter, μ: dynamic viscosity). The Womersley number may be defined as

$\frac{D}{2}\sqrt{\frac{\varpi\;\rho}{\mu}}$where (ω: angular frequency, equal to

$ \frac{1}{{cardiac}\mspace{14mu}{cycle}\mspace{14mu}{length}} ).$

Method 400 may further include obtaining an indication of the presenceor absence of plaque at one or more locations of the patient-specificgeometric model (step 410). For example, in one embodiment, the locationof calcified or non-calcified plaque may be determined using CT and/orother imaging modalities, including intravascular ultrasound, or opticalcoherence tomography. For example, the plaque may be detected in thethree-dimensional image (200 of FIG. 2) generated from patient-specificanatomical data. The plaque may be identified in a three-dimensionalimage or model as areas that are lighter than the lumens of the aorta,the main coronary arteries, and/or the branches. Thus, the plaque may bedetected by the computer system as having an intensity value below a setvalue or may be detected visually by the user. The location of detectedplaques may be parameterized by a distance from the ostium point (leftmain or right coronary ostium) to the projection of centroid of plaquecoordinates onto the associated vessel centerline and an angularposition of plaque with respect to myocardium (e.g.,myocardial/pericardial side). The location of detected plaques may bealso parameterized by start and end points of the projection of plaquecoordinates onto the associated vessel centerline. If plaque exists at alocation, method 400 may include obtaining a list of one or moremeasurements of coronary plaque composition, e.g., type, Hounsfieldunits (“HU”), etc., burden, shape (eccentric or concentric), andlocation.

Method 400 may further include, for each of a plurality of points in thepatient-specific geometric model for which there is information aboutthe presence or absence of plaque (step 412), creating a feature vectorfor the point (step 414) and associating the feature vector with thepresence or absence of plaque at that point (step 416). In oneembodiment, the step of creating a feature vector for the point mayinclude creating a feature vector for that point that consists of anumerical description of the geometry and biophysical hemodynamiccharacteristics at that point, and estimates of physiological orphenotypic parameters of the patient. For example, a feature vector forattributes: distance to ostium, wall shear stress, local flow rate,Reynolds number, and centerline curvature, may be in the form of (50 mm,70 dyne/cm², 1500 mm³/sec, 400, 1 mm⁻¹). Global physiological orphenotypic parameters may be used in the feature vector of all points,and local physiological or phenotypic parameters may change in thefeature vector of different points.

In one embodiment, an exemplary feature vector generated in step 414 mayinclude one or more of: (i) systolic and diastolic blood pressure, (ii)heart rate, (iii) blood properties including: plasma, red blood cells(erythrocytes), hematocrit, white blood cells (leukocytes) and platelets(thrombocytes), viscosity, yield stress, etc. (iv) patient age, gender,height, weight, etc., (v) lifestyle characteristics, e.g., presence orabsence of current medications/drugs, (vi) general risk factors of CAD,such as smoking, diabetes, hypertension, abdominal obesity, dietaryhabits, family history of CAD, etc., (vii) in vitro blood test results,such as LDL, Triglyceride cholesterol level, etc., (viii) coronarycalcium score, (ix) amount of calcium in aorta and valve, (x) presenceof aortic aneurysm, (xi) presence of valvular heart disease, (xii)presence of peripheral disease, (xiii) presence of dental disease, (xiv)epicardial fat volume, (xv) cardiac function (ejection fraction), (xvi)stress echocardiogram test results, (xvii) characteristics of the aorticgeometry (e.g., cross-sectional area profile along the ascending anddescending aorta, and surface area and volume of the aorta, (xviii) aSYNTAX score, as described in U.S. patent application Ser. No.13/656,183, filed by Timothy A. Fonte et al. on Oct. 19, 2012, theentire disclosure of which is incorporated herein by reference, (xix)plaque burden of existing plaque, (xx) adverse plaque characteristics ofexisting plaque (e.g., presence of positive remodeling, presence of lowattenuation plaque, presence of spotty calcification), (xxi)characteristics of the coronary branch geometry, (xxii) characteristicsof coronary cross-sectional area, (xxiii) characteristics of coronarylumen intensity, e.g., intensity change along the centerline (slope oflinearly-fitted intensity variation), (xxiv) characteristics of surfaceof coronary geometry, e.g., 3D surface curvature of geometry (Gaussian,maximum, minimum, mean), (xxv) characteristics of volume of coronarygeometry, e.g., ratio of total coronary volume compared to myocardialvolume, (xxvi) characteristics of coronary centerline, (xxvii)characteristics of coronary deformation, (xxviii) characteristics ofexisting plaque, and (xxix) characteristics of coronary hemodynamicsderived from computational flow dynamics or invasive measurement.

In one embodiment, the characteristics of the coronary branch geometrymay include one or more of: (1) total number of vessel bifurcations, andthe number of upstream/downstream vessel bifurcations; (2) average,minimum, and maximum upstream/downstream cross-sectional areas; (3)distances (along the vessel centerline) to the centerline point ofminimum and maximum upstream/downstream cross-sectional areas, (4)cross-sectional area of and distance (along the vessel centerline) tothe nearest upstream/downstream vessel bifurcation, (5) cross-sectionalarea of and distance (along the vessel centerline) to the nearestcoronary outlet and aortic inlet/outlet, (6) cross-sectional areas anddistances (along the vessel centerline) to the downstream coronaryoutlets with the smallest/largest cross-sectional areas, and/or (7)upstream/downstream volumes of the coronary vessels.

In one embodiment, the characteristics of coronary cross-sectional areamay include one or more of: (1) cross-sectional lumen area along thecoronary centerline, (2) cross-sectional lumen area to the power of N(where N can be determined from various source of scaling laws such asMurray's law (N=1.5) and Uylings' study (N=1.165˜1.5)), (3) a ratio oflumen cross-sectional area with respect to the main ostia (LM, RCA)(e.g., measure of cross-sectional area at the LM ostium, normalizedcross-sectional area of the left coronary by LM ostium area, measure ofcross-sectional area at the RCA ostium, normalized cross-sectional areaof the right coronary by RCA ostium area), (4) ratio of lumencross-sectional area with respect to the main ostia to the power of N(where N can be determined from various sources of scaling laws such asMurray's law (N=1.5) and Uyling's study (N=1.165˜1.5)), (5) degree oftapering in cross-sectional lumen area along the centerline (based on asample centerline points within a certain interval (e.g., twice thediameter of the vessel) and computation of a slope of linearly-fittedcross-sectional area), (6) location of stenotic lesions (based ondetecting minima of cross-sectional area curve (e.g., detectinglocations, where first derivative of area curve is zero and secondderivative is positive, and smoothing cross-sectional area profile toavoid detecting artifactual peaks), and computing distance (parametricarc length of centerline) from the main ostium, (7) length of stenoticlesions (computed based on the proximal and distal locations from thestenotic lesion, where cross-sectional area is recovered), (8) degree ofstenotic lesions, by evaluating degree of stenosis based on referencevalues of smoothed cross-sectional area profile using Fourier smoothingor kernel regression, (9) location and number of lesions correspondingto 50%, 75%, 90% area reduction, (10) distance from stenotic lesion tothe main ostia, and/or (11) irregularity (or circularity) ofcross-sectional lumen boundary.

In one embodiment, the characteristics of coronary centerline mayinclude: (1) curvature (bending) of coronary centerline, such as bycomputing Frenet curvature, based on

${\kappa = \frac{{p^{\prime} \times p^{''}}}{{p^{\prime}}^{3}}},$where ρ is a coordinate of the centerline, and computing an inverse ofthe radius of a circumscribed circle along the centerline points, and(2) tortuosity (non-planarity) of coronary centerline, such as bycomputing Frenet torsion, based on

${\tau = \frac{( {p^{\prime} \times p^{''}} ) \cdot p^{\prime\prime\prime}}{{{p^{\prime} \times p^{''}}}^{2}}},$where ρ is a coordinate of the centerline.

In one embodiment, calculation of the characteristics of coronarydeformation may involve multi-phase CCTA (e.g., diastole and systole),including (1) distensibility of coronary artery over cardiac cycle, (2)bifurcation angle change over cardiac cycle, and/or (3) curvature changeover cardiac cycle. In one embodiment, the characteristics of existingplaque may be calculated based on: (1) volume of plaque, (2) intensityof plaque, (3) type of plaque (calcified, non-calcified), (4) distancefrom the plaque location to ostium (LM or RCA), and (5) distance fromthe plaque location to the nearest downstream/upstream bifurcation.

In one embodiment, the characteristics of coronary hemodynamics may bederived from computational flow dynamics or invasive measurement. Forexample, pulsatile flow simulation may be performed to obtain transientcharacteristics of blood, by using a lumped parameter coronary vascularmodel for downstream vasculatures, inflow boundary condition withcoupling a lumped parameter heart model and a closed loop model todescribe the intramyocardial pressure variation resulting from theinteractions between the heart and arterial system during cardiac cycle.For example, the calculation may include: measured FFR, coronary flowreserve, pressure distribution, FFRct, mean wall-shear stress,oscillatory shear index, particle residence time, turbulent kineticenergy, Reynolds number, Womersley number, and/or local flow rate.

Method 400 may then include associating the feature vector with thepresence or absence of plaque at each point of the patient-specificgeometric model (step 416). Method 400 may involve continuing to performthe above steps 412, 414, 416, for each of a plurality of points in thepatient-specific geometric model (step 418), and for each of any numberof patients on which a machine learning algorithm may be based (step420). Method 400 may then include training the machine learningalgorithm to predict the probability of the presence of plaque at thepoints from the feature vectors at the points (step 422). Examples ofmachine learning algorithms suitable for performing this task mayinclude support vector machines (SVMs), multi-layer perceptrons (MLPs),and/or multivariate regression (MVR) (e.g., weighted linear or logisticregression).

Method 400 may then include storing or otherwise saving the results ofthe machine learning algorithm (e.g., feature weights) to a digitalrepresentation, such as the memory or digital storage (e.g., hard drive,network drive) of a computational device, such as a computer, laptop,DSP, server, etc. of server systems 106 (step 424).

FIG. 4B is a block diagram of an exemplary method 450 for using amachine learning system trained according to method 400 (e.g., a machinelearning system 310 executed on server systems 106) for predicting, fora particular patient, the location of coronary lesions from vesselgeometry, physiology, and hemodynamics, according to an exemplaryembodiment of the present disclosure. In one embodiment, method 450 mayinclude, for one or more patients (step 452), obtaining apatient-specific geometric model of a portion of the patient'svasculature (step 454), obtaining one or more estimates of physiologicalor phenotypic parameters of the patient (step 456), and obtaining one ormore estimates of biophysical hemodynamic characteristics of the patient(step 458).

Specifically, the step of obtaining a patient-specific geometric modelof a portion of the patient's vasculature (step 454) may includeobtaining a patient-specific model of the geometry for one or more ofthe patient's blood vessels, myocardium, aorta, valves, plaques, and/orchambers. In one embodiment, this geometry may be represented as a listof points in space (possibly with a list of neighbors for each point) inwhich the space can be mapped to spatial units between points (e.g.,millimeters). In one embodiment, this model may be derived by performinga cardiac CT imaging of the patient in the end diastole phase of thecardiac cycle. This image then may be segmented manually orautomatically to identify voxels belonging to the aorta and the lumen ofthe coronary arteries. Inaccuracies in the geometry extractedautomatically may be corrected by a human observer who compares theextracted geometry with the images and makes corrections as needed. Oncethe voxels are identified, the geometric model can be derived (e.g.,using marching cubes).

In one embodiment, the step of obtaining one or more estimates ofphysiological or phenotypic parameters of the patient (step 456) mayinclude obtaining a list of one or more estimates of physiological orphenotypic parameters of the patient, such as blood pressure, bloodviscosity, in vitro blood test results (e.g., LDL/Triglyceridecholesterol level), patient age, patient gender, the mass of thesupplied tissue, etc. These parameters may be global (e.g., bloodpressure) or local (e.g., estimated density of the vessel wall at alocation). In one embodiment, the physiological or phenotypic parametersmay include, blood pressure, hematocrit level, patient age, patientgender, myocardial mass (e.g., derived by segmenting the myocardium inthe image, and calculating the volume in the image and using anestimated density of 1.05 g/mL to estimate the myocardial mass), generalrisk factors of coronary artery disease (e.g., smoking, diabetes,hypertension, abdominal obesity, dietary habits, family history, etc.),and/or in vitro blood test results (e.g., LDL, Triglyceride cholesterollevel).

In one embodiment, the step of obtaining one or more estimates ofbiophysical hemodynamic characteristics of the patient (step 458) mayinclude obtaining a list of one or more estimates of biophysicalhemodynamic characteristics from computational fluid dynamics analysis,such as wall-shear stress, oscillatory shear index, particle residencetime, Reynolds number, Womersley number, local flow rate, and turbulentkinetic energy, etc. Specifically, the mean wall-shear stress, may bedefined as

${{{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{\overset{arrow}{t_{s}}{\mathbb{d}t}}}}} \cdot \overset{arrow}{t_{s}}},$which may be the wall shear stress vector defined as the in-planecomponent of the surface traction vector. The oscillatory shear index(OSI), may be defined as

${\frac{1}{2}( {1 - \frac{{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{\overset{arrow}{t_{s}}{\mathbb{d}t}}}}}{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{{\overset{arrow}{t_{s}}}{\mathbb{d}t}}}}} )},$which may be a measure of the uni-directionality of shear stress. Theparticle residence time may be a measure of the time it takes blood tobe flushed from a specified fluid domain. The turbulent kinetic energy(TKE) may be a measure of the intensity of turbulence associated witheddies in turbulent flow, and may be characterized by measuredroot-mean-square velocity fluctuation, and may be normalized by kineticenergy. The Reynolds number may be defined as

$\frac{\rho\;{UD}}{\mu}$where (ρ: density of blood, U: average flow velocity, D: vesseldiameter, μ: dynamic viscosity). The Womersley number may be defined as

$\frac{D}{2}\sqrt{\frac{\overset{\_}{\omega}\;\rho}{\mu}}$where (ω: angular frequency, equal to

$ \frac{1}{{cardiac}\mspace{14mu}{cycle}\mspace{14mu}{length}} ).$

Method 450 may include, for every point in the patient-specificgeometric model of the patient (step 460), creating for that point afeature vector comprising a numerical description of the geometry andbiophysical hemodynamic characteristic at that point, and estimates ofphysiological or phenotypic parameters of the patient (step 462). Globalphysiological or phenotypic parameters may be used in the feature vectorof one or more points, and local physiological or phenotypic parametersmay change in the feature vector of different points. Method 450 mayinvolve continuing to perform the above steps 460, 462, for each of aplurality of points in the patient-specific geometric model (step 464).

Method 450 may then include producing estimates of the probability ofthe presence or absence of plaque at each point in the patient-specificgeometric model based on the stored machine learning results (stored atB, FIG. 4A) (step 468). Specifically, method 450 may use the savedresults of the machine learning algorithm 310 produced in the trainingmode of method 400 (e.g., feature weights) to produce estimates of theprobability of the presence of plaque at each point in thepatient-specific geometric model (e.g., by generating plaque estimatesas a function of the feature vector at each point). These estimates maybe produced using the same machine learning algorithm technique used inthe training mode (e.g., the SVM, MLP, MVR technique). In oneembodiment, the estimates may be a probability of the existence ofplaque at each point of a geometric model. If there is no existingplaque at a point, the method may include generating an estimatedprobability of the onset of plaque (e.g., lipid-rich, non-calcifiedplaque). If plaque does exist at a point, the method may includegenerating an estimated probability of progression of the identifiedplaque to a different stage (e.g., fibrotic or calcified), and theamount or shape of such progression. In one embodiment, the estimatesmay be a probability of a shape, type, composition, size, growth, and/orshrinkage of plaque at any given location or combination of locations.For example, in one embodiment, (in the absence of longitudinal trainingdata) the progression of plaque may be predicted by determining that thepatient appears that they should have disease characteristic X based onthe patient's population, despite actually having characteristic Y.Therefore, the estimate may include a prediction that the patient willprogress from state X to state Y, which may include assumptions and/orpredictions about plaque growth, shrinkage, change of type, change ofcomposition, change of shape, etc.). Method 450 may then include savingthe estimates of the probability of the presence or absence of plaque(step 470), such as to the memory or digital storage (e.g., hard drive,network drive) of a computational device, such as a computer, laptop,DSP, server, etc., of server systems 106, and communicating thesepatient-specific and location-specific predicted probabilities of lesionformation to a health care provider, such as over electronic network101.

FIG. 5A is a block diagram of an exemplary method 500 for training amachine learning system (e.g., a machine learning system 310 executed onserver systems 106) for predicting the onset or change (e.g., growthand/or shrinkage), of coronary lesions over time, such as by usinglongitudinal data (i.e., corresponding data taken from the same patientsat different points in time) of vessel geometry, physiology, andhemodynamics, according to an exemplary embodiment of the presentdisclosure. Specifically, method 500 may include, for one or morepatients (step 502), obtaining a patient-specific geometric model of aportion of the patient's vasculature (step 504), obtaining one or moreestimates of physiological or phenotypic parameters of the patient (step506), and obtaining one or more estimates of biophysical hemodynamiccharacteristics of the patient (step 508).

For example, the step of obtaining a patient-specific geometric model ofa portion of the patient's vasculature (step 504) may include obtaininga patient-specific model of the geometry for one or more of thepatient's blood vessels, myocardium, aorta, valves, plaques, and/orchambers. In one embodiment, this geometry may be represented as a listof points in space (possibly with a list of neighbors for each point) inwhich the space can be mapped to spatial units between points (e.g.,millimeters). In one embodiment, this model may be derived by performinga cardiac CT imaging of the patient in the end diastole phase of thecardiac cycle. This image then may be segmented manually orautomatically to identify voxels belonging to the aorta and the lumen ofthe coronary arteries. Inaccuracies in the geometry extractedautomatically may be corrected by a human observer who compares theextracted geometry with the images and makes corrections as needed. Oncethe voxels are identified, the geometric model can be derived (e.g.,using marching cubes).

The step of obtaining one or more estimates of physiological orphenotypic parameters of the patient (step 506) may include obtaining alist of one or more estimates of physiological or phenotypic parametersof the patient, such as blood pressure, blood viscosity, in vitro bloodtest results (e.g., LDL/Triglyceride cholesterol level), patient age,patient gender, the mass of the supplied tissue, etc. These parametersmay be global (e.g., blood pressure) or local (e.g., estimated densityof the vessel wall at a location). In one embodiment, the physiologicalor phenotypic parameters may include, blood pressure, hematocrit level,patient age, patient gender, myocardial mass (e.g., derived bysegmenting the myocardium in the image, and calculating the volume inthe image and using an estimated density of 1.05 g/mL to estimate themyocardial mass), general risk factors of coronary artery disease (e.g.,smoking, diabetes, hypertension, abdominal obesity, dietary habits,family history, etc.), and/or in vitro blood test results (e.g., LDL,Triglyceride cholesterol level).

The step of obtaining one or more estimates of biophysical hemodynamiccharacteristics of the patient (step 508) may include obtaining a listof one or more estimates of biophysical hemodynamic characteristics fromcomputational fluid dynamics analysis, such as wall-shear stress,oscillatory shear index, particle residence time, Reynolds number,Womersley number, local flow rate, and turbulent kinetic energy, etc.Specifically, the mean wall-shear stress, may be defined as

${{{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{\overset{arrow}{t_{s}}\ {\mathbb{d}t}}}}} \cdot \overset{arrow}{t_{s}}},$which may be the wall shear stress vector defined as the in-planecomponent of the surface traction vector. The oscillatory shear index(OSI), may be defined as

${\frac{1}{2}( {1 - \frac{{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{\overset{arrow}{t_{s}}\ {\mathbb{d}t}}}}}{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{{\overset{arrow}{t_{s}}\ }{\mathbb{d}t}}}}} )},$which may be a measure of the uni-directionality of shear stress. Theparticle residence time may be a measure of the time it takes blood tobe flushed from a specified fluid domain. The turbulent kinetic energy(TKE) may be a measure of the intensity of turbulence associated witheddies in turbulent flow, and may be characterized by measuredroot-mean-square velocity fluctuation, and may be normalized by kineticenergy. The Reynolds number may be defined as

$\frac{\rho\;{UD}}{\mu}$where (ρ: density of blood, U: average flow velocity, D: vesseldiameter, μ: dynamic viscosity). The Womersley number may be defined as

$\frac{D}{2}\sqrt{\frac{\overset{\_}{\omega}\;\rho}{\mu}}$where (ω: angular frequency, equal to

$ \frac{1}{{cardiac}\mspace{14mu}{cycle}\mspace{14mu}{length}} ).$

Method 500 may further include obtaining an indication of the growth,shrinkage, or onset of plaque at one or more locations of thepatient-specific geometric model (step 510). For example, in oneembodiment, the location of plaque may be determined using CT and/orother imaging modalities, including intravascular ultrasound, or opticalcoherence tomography. If plaque exists at a location, method 500 mayinclude obtaining a list of one or more measurements of coronary plaquecomposition, burden and location.

In order to synchronize geometry obtained from patients over time, itmay be desirable to determine point correspondence between multiple timevariant scans of each individual. In other words, it may be desirable tolearn the vessel characteristics in a location at the earlier time pointthat are correlated with the progression of disease in the same locationat the later time point, such as by using a database of pairs of imagesof the same patient at two different time points. Given the image of anew patient, training data of local disease progression may then be usedto predict the change in disease at each location. Accordingly, in oneembodiment, step 510 may further include: (i) determining a mapping of acoronary centerline from an initial scan to a follow-up scan; and (ii)determining a mapping of extracted plaques using curvilinear coordinatesdefined along the centerline. In one embodiment, the coronary centerlinemapping may be determined by (i) extracting centerlines of majorepicardial coronary arteries (e.g., left descending coronary artery,circumlex artery, right coronary artery) and branch vessels (e.g,diagonal, marginal, etc) for each scan; (ii) using bifurcating points asfiducial landmarks to determine common material points between thescans; and (iii) for points between bifurcations, using linearinterpolation or cross-sectional area profile (e.g., value, slope) ofcoronary vessels to identify correspondence. In one embodiment, themapping of extracted plaques may be determined by: (i) extracting plaquefrom each scan; (ii) parameterizing the location of plaque voxels bycurvilinear coordinate system for each associated centerline (r,θ,s);and determining correspondence of plaque voxels in each curvilinearcoordinate system. In one embodiment, the curvilinear coordinate systemmay be defined where:

r=distance from plaque voxel to the associated centerline (projection ofplaque);

s=distance from ostium point (Left main or right coronary) to theprojection of plaque voxel onto associated centerline; and

θ=angular position with respect to reference parallel path tocenterline.

Method 500 may further include, for each of a plurality of points in thepatient-specific geometric model for which there is information aboutthe growth, shrinkage, or onset of plaque (step 512), creating a featurevector for the point (step 514) and associating the feature vector withthe growth, shrinkage, or onset of plaque at that point (step 516). Inone embodiment, the step of creating a feature vector for the point mayinclude creating a feature vector for that point that consists of anumerical description of the geometry and biophysical hemodynamiccharacteristics at that point, and estimates of physiological orphenotypic parameters of the patient. For example, a feature vector forattributes: hematocrit, plaque burden, plaque Hounsfield unit, distanceto ostium, wall shear stress, flow, Reynolds number, and centerlinecurvature may be in the form of: (45%, 20 mm³, 130 HU, 60.5 mm, 70dyne/cm², 1500 mm³/sec, 400, 1 mm⁻¹). Global physiological or phenotypicparameters may be used in the feature vector of all points, and localphysiological or phenotypic parameters may change in the feature vectorof different points.

In one embodiment, an exemplary feature vector generated in step 514 mayinclude one or more of: (i) systolic and diastolic blood pressure, (ii)heart rate, (iii) blood properties including: plasma, red blood cells(erythrocytes), hematocrit, white blood cells (leukocytes) and platelets(thrombocytes), viscosity, yield stress, etc. (iv) patient age, gender,height, weight, etc., (v) lifestyle characteristics, e.g., presence orabsence of current medications/drugs, (vi) general risk factors of CAD,such as smoking, diabetes, hypertension, abdominal obesity, dietaryhabits, family history of CAD, etc., (vii) in vitro blood test results,such as LDL, Triglyceride cholesterol level, etc., (viii) coronarycalcium score, (ix) amount of calcium in aorta and valve, (x) presenceof aortic aneurysm, (xi) presence of valvular heart disease, (xii)presence of peripheral disease, (xiii) presence of dental disease, (xiv)epicardial fat volume, (xv) cardiac function (ejection fraction), (xvi)stress echocardiogram test results, (xvii) characteristics of the aorticgeometry (e.g., cross-sectional area profile along the ascending anddescending aorta, and Surface area and volume of the aorta, (xviii) aSYNTAX score, as described above, (xix) plaque burden of existingplaque, (xx) adverse plaque characteristics of existing plaque (e.g.,presence of positive remodeling, presence of low attenuation plaque,presence of spotty calcification), (xxi) characteristics of the coronarybranch geometry, (xxii) characteristics of coronary cross-sectionalarea, (xxiii) characteristics of coronary lumen intensity, e.g.,intensity change along the centerline (slope of linearly-fittedintensity variation), (xxiv) characteristics of surface of coronarygeometry, e.g., 3D surface curvature of geometry (Gaussian, maximum,minimum, mean), (xxv) characteristics of volume of coronary geometry,e.g., ratio of total coronary volume compared to myocardial volume,(xxvi) characteristics of coronary centerline, (xxvii) characteristicsof coronary deformation, (xxviii) characteristics of existing plaque,and/or (xxix) characteristics of coronary hemodynamics derived fromcomputational flow dynamics or invasive measurement.

In one embodiment, the characteristics of the coronary branch geometrymay include one or more of: (1) total number of vessel bifurcations, andthe number of upstream/downstream vessel bifurcations; (2) average,minimum, and maximum upstream/downstream cross-sectional areas; (3)distances (along the vessel centerline) to the centerline point ofminimum and maximum upstream/downstream cross-sectional areas, (4)cross-sectional area of and distance (along the vessel centerline) tothe nearest upstream/downstream vessel bifurcation, (5) cross-sectionalarea of and distance (along the vessel centerline) to the nearestcoronary outlet and aortic inlet/outlet, (6) cross-sectional areas anddistances (along the vessel centerline) to the downstream coronaryoutlets with the smallest/largest cross-sectional areas, and/or (7)upstream/downstream volumes of the coronary vessels.

In one embodiment, the characteristics of coronary cross-sectional areamay include one or more of: (1) cross-sectional lumen area along thecoronary centerline, (2) cross-sectional lumen area to the power of N(where N can be determined from various source of scaling laws such asMurray's law (N=1.5) and Uylings' study (N=1.165˜1.5)), (3) a ratio oflumen cross-sectional area with respect to the main ostia (LM, RCA)(e.g., measure of cross-sectional area at the LM ostium, normalizedcross-sectional area of the left coronary by LM ostium area, measure ofcross-sectional area at the RCA ostium, normalized cross-sectional areaof the right coronary by RCA ostium area, (4) ratio of lumencross-sectional area with respect to the main ostia to the power of N(where power can be determined from various source of scaling laws suchas Murray's law (N=1.5) and Uylings' study (N=1.165˜1.5)), (5) degree oftapering in cross-sectional lumen area along the centerline (based on asample centerline points within a certain interval (e.g., twice thediameter of the vessel) and compute a slope of linearly-fittedcross-sectional area), (6) location of stenotic lesions (based ondetecting minima of cross-sectional area curve (e.g., detectinglocations, where first derivative of area curve is zero and secondderivative is positive, and smoothing cross-sectional area profile toavoid detecting artifactual peaks), and computing distance (parametricarc length of centerline) from the main ostium, (7) length of stenoticlesions (computed based on the proximal and distal locations from thestenotic lesion, where cross-sectional area is recovered, (8) degree ofstenotic lesions, by evaluating degree of stenosis based on referencevalues of smoothed cross-sectional area profile using Fourier smoothingor kernel regression, (9) location and number of lesions correspondingto 50%, 75%, 90% area reduction, (10) distance from stenotic lesion tothe main ostia, and/or (11) irregularity (or circularity) ofcross-sectional lumen boundary.

In one embodiment, the characteristics of coronary centerline mayinclude: (1) curvature (bending) of coronary centerline, such as bycomputing Frenet curvature, based on

${\kappa = \frac{{p^{\prime} \times p^{''}}}{{p^{\prime}}^{3}}},$where ρ is a coordinate of the centerline, and computing an inverse ofthe radius of a circumscribed circle along the centerline points, and/or(2) tortuosity (non-planarity) of coronary centerline, such as bycomputing Frenet torsion, based on

${\tau = \frac{( {p^{\prime} \times p^{''}} ) \cdot p^{''\prime}}{{{p^{\prime} \times p^{''}}}^{2}}},$where ρ is a coordinate of the centerline.

In one embodiment, calculation of the characteristics of coronarydeformation may involve multi-phase CCTA (e.g., diastole and systole),including (1) distensibility of coronary artery over cardiac cycle, (2)bifurcation angle change over cardiac cycle, and/or (3) curvature changeover cardiac cycle. In one embodiment, the characteristics of existingplaque may be calculated based on: (1) volume of plaque, (2) intensityof plaque, (3) type of plaque (calcified, non-calcified), (4) distancefrom the plaque location to ostium (LM or RCA), and/or (5) distance fromthe plaque location to the nearest downstream/upstream bifurcation.

In one embodiment, the characteristics of coronary hemodynamics may bederived from computational flow dynamics or invasive measurement. Forexample, pulsatile flow simulation may be performed to obtain transientcharacteristics of blood, by using a lumped parameter coronary vascularmodel for downstream vasculatures, inflow boundary condition withcoupling a lumped parameter heart model and a closed loop model todescribe the intramyocardial pressure variation resulting from theinteractions between the heart and arterial system during cardiac cycle.For example, the calculation may include one or more of: measured FFR,coronary flow reserve, pressure distribution, FFRct, mean wall-shearstress, oscillatory shear index, particle residence time, turbulentkinetic energy, Reynolds number, Womersley number, and/or local flowrate.

Method 500 may then include associating the feature vector with thegrowth, shrinkage, or onset of plaque at each point of thepatient-specific geometric model (step 516). Method 500 may involvecontinuing to perform the above steps 512, 514, 516, for each of aplurality of points in the patient-specific geometric model (step 518),and for each of any number of patients for which a machine learningalgorithm may be based (step 520). Method 500 may also involvecontinuing to perform the above steps 512, 514, 516, for each of aplurality of points in the patient-specific geometric model, and foreach of any number of patients for which a machine learning algorithmmay be based, across any additional time period or periods useful forgenerating information about the growth, shrinkage, or onset of plaque(i.e., the change and/or rate of change of plaque at each point of themodel) (step 522).

Method 500 may then include training a machine learning algorithm topredict the probability of amounts of growth, shrinkage, or onset ofplaque at the points from the feature vectors at the points (step 524).Examples of machine learning algorithms suitable for performing thistask may include support vector machines (SVMs), multi-layer perceptrons(MLPs), and/or multivariate regression (MVR) (e.g., weighted linear orlogistic regression). In one embodiment, if training data causes themachine learning algorithm to predict a lower amount (e.g., size orextent) of plaque than what is detected, then the machine learningalgorithm may be interpreted as predicting plaque shrinkage; if trainingdata causes the machine learning algorithm to predict a higher amount(e.g., size or extent) of plaque than what is detected, then the machinelearning algorithm may be interpreted as predicting plaque growth.

Method 500 may then include storing or otherwise saving the results ofthe machine learning algorithm (e.g., feature weights) to a digitalrepresentation, such as the memory or digital storage (e.g., hard drive,network drive) of a computational device, such as a computer, laptop,DSP, server, etc. of server systems 106 (step 526).

FIG. 5B is a block diagram of an exemplary method of using the machinelearning system (e.g., machine learning system 310 executed on serversystems 106) for predicting, for a particular patient, the rate ofonset, growth/shrinkage, of coronary lesions from vessel geometry,physiology, and hemodynamics, according to an exemplary embodiment ofthe present disclosure. In one embodiment, method 550 may include, forone or more patients (step 552), obtaining a patient-specific geometricmodel of a portion of the patient's vasculature (step 554), obtainingone or more estimates of physiological or phenotypic parameters of thepatient (step 556), and obtaining one or more estimates of biophysicalhemodynamic characteristics of the patient (step 558).

Specifically, the step of obtaining a patient-specific geometric modelof a portion of the patient's vasculature (step 554) may includeobtaining a patient-specific model of the geometry for one or more ofthe patient's blood vessels, myocardium, aorta, valves, plaques, and/orchambers. In one embodiment, this geometry may be represented as a listof points in space (possibly with a list of neighbors for each point) inwhich the space can be mapped to spatial units between points (e.g.,millimeters). In one embodiment, this model may be derived by performinga cardiac CT imaging of the patient in the end diastole phase of thecardiac cycle. This image then may be segmented manually orautomatically to identify voxels belonging to the aorta and the lumen ofthe coronary arteries. Inaccuracies in the geometry extractedautomatically may be corrected by a human observer who compares theextracted geometry with the images and makes corrections as needed. Oncethe voxels are identified, the geometric model can be derived (e.g.,using marching cubes).

In one embodiment, the step of obtaining one or more estimates ofphysiological or phenotypic parameters of the patient (step 556) mayinclude obtaining a list of one or more estimates of physiological orphenotypic parameters of the patient, such as blood pressure, bloodviscosity, in vitro blood test results (e.g., LDL/Triglyceridecholesterol level), patient age, patient gender, the mass of thesupplied tissue, etc. These parameters may be global (e.g., bloodpressure) or local (e.g., estimated density of the vessel wall at alocation). In one embodiment, the physiological or phenotypic parametersmay include, blood pressure, hematocrit level, patient age, patientgender, myocardial mass (e.g., derived by segmenting the myocardium inthe image, and calculating the volume in the image and using anestimated density of 1.05 g/mL to estimate the myocardial mass), generalrisk factors of coronary artery disease (e.g., smoking, diabetes,hypertension, abdominal obesity, dietary habits, family history, etc.),and/or in vitro blood test results (e.g., LDL, Triglyceride cholesterollevel).

In one embodiment, the step of obtaining one or more estimates ofbiophysical hemodynamic characteristics of the patient (step 558) mayinclude obtaining a list of one or more estimates of biophysicalhemodynamic characteristics from computational fluid dynamics analysis,such as wall-shear stress, oscillatory shear index, particle residencetime, Reynolds number, Womersley number, local flow rate, and turbulentkinetic energy, etc. Specifically, the mean wall-shear stress, may bedefined as

${{{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{\overset{arrow}{t_{s}}\ {\mathbb{d}t}}}}} \cdot \overset{arrow}{t_{s}}},$which may be the wall shear stress vector defined as the in-planecomponent of the surface traction vector. The oscillatory shear index(OSI), may be defined as

${\frac{1}{2}( {1 - \frac{{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{\overset{arrow}{t_{s}}\ {\mathbb{d}t}}}}}{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{{\overset{arrow}{t_{s}}\ }{\mathbb{d}t}}}}} )},$which may be a measure of the uni-directionality of shear stress. Theparticle residence time may be a measure of the time it takes blood tobe flushed from a specified fluid domain. The turbulent kinetic energy(TKE) may be a measure of the intensity of turbulence associated witheddies in turbulent flow, and may be characterized by measuredroot-mean-square velocity fluctuation, and may be normalized by kineticenergy. The Reynolds number may be defined as

$\frac{\rho\;{UD}}{\mu}$where (ρ: density of blood, U: average flow velocity, D: vesseldiameter, μ: dynamic viscosity). The Womersley number may be defined as

$\frac{D}{2}\sqrt{\frac{\overset{\_}{\omega}\;\rho}{\mu}}$where (ω: angular frequency, equal to

$ \frac{1}{{cardiac}\mspace{14mu}{cycle}\mspace{14mu}{length}} ).$

Method 550 may include, for every point in the patient-specificgeometric model (step 560), creating for that point a feature vectorcomprising a numerical description of the geometry and biophysicalhemodynamic characteristic at that point, and estimates of physiologicalor phenotypic parameters of the patient. Global physiological orphenotypic parameters can be used in the feature vector of all pointsand local physiological or phenotypic parameters can change in thefeature vector of different points. Method 550 may involve continuing toperform the above steps 560, 562, for each of a plurality of points inthe patient-specific geometric model (step 564).

Method 550 may then include producing estimates of the probabilityand/or rate of the growth, shrinkage, or onset of plaque at each pointin the patient-specific geometric model based on the stored machinelearning results (stored at B, FIG. 5A) (step 566). Specifically, method550 may use the saved results of the machine learning algorithm producedin the training mode of method 500 (e.g., feature weights) to produceestimates of the probability of growth, shrinkage, or onset (e.g., ratesof growth/shrinkage) of plaque at each point in the patient-specificgeometric model (e.g., by generating plaque estimates as a function ofthe feature vector at each point). These estimates may be produced usingthe same machine learning algorithm technique used in the training mode(e.g., the SVM, MLP, MVR technique). Method 550 may then include savingthe estimates of the probability of the growth, shrinkage, or onset ofplaque (step 568), such as to the memory or digital storage (e.g., harddrive, network drive) of a computational device, such as a computer,laptop, DSP, server, etc., of server systems 106, and communicatingthese patient-specific and location-specific predicted probabilities oflesion formation to a health care provider.

FIG. 6 is a simplified block diagram of an exemplary computer system 600in which embodiments of the present disclosure may be implemented, forexample as any of the physician devices or servers 102, third partydevices or servers 104, and server systems 106. A platform for a server600, for example, may include a data communication interface for packetdata communication 660. The platform may also include a centralprocessing unit (CPU) 620, in the form of one or more processors, forexecuting program instructions. The platform typically includes aninternal communication bus 610, program storage and data storage forvarious data files to be processed and/or communicated by the platformsuch as ROM 630 and RAM 640, although the server 600 often receivesprogramming and data via a communications network (not shown). Thehardware elements, operating systems and programming languages of suchequipment are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith. The server 600also may include input and output ports 650 to connect with input andoutput devices such as keyboards, mice, touchscreens, monitors,displays, etc. Of course, the various server functions may beimplemented in a distributed fashion on a number of similar platforms,to distribute the processing load. Alternatively, the servers may beimplemented by appropriate programming of one computer hardwareplatform.

As described above, the computer system 600 may include any type orcombination of computing systems, such as handheld devices, personalcomputers, servers, clustered computing machines, and/or cloud computingsystems. In one embodiment, the computer system 600 may be an assemblyof hardware, including a memory, a central processing unit (“CPU”),and/or optionally a user interface. The memory may include any type ofRAM or ROM embodied in a physical storage medium, such as magneticstorage including floppy disk, hard disk, or magnetic tape;semiconductor storage such as solid state disk (SSD) or flash memory;optical disc storage; or magneto-optical disc storage. The CPU mayinclude one or more processors for processing data according toinstructions stored in the memory. The functions of the processor may beprovided by a single dedicated processor or by a plurality ofprocessors. Moreover, the processor may include, without limitation,digital signal processor (DSP) hardware, or any other hardware capableof executing software. The user interface may include any type orcombination of input/output devices, such as a display monitor,touchpad, touchscreen, microphone, camera, keyboard, and/or mouse.

Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine readable medium. “Storage” type media include any or all of thetangible memory of the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide non-transitory storage atany time for the software programming. All or portions of the softwaremay at times be communicated through the Internet or various othertelecommunication networks. Such communications, for example, may enableloading of the software from one computer or processor into another, forexample, from a management server or host computer of the mobilecommunication network into the computer platform of a server and/or froma server to the mobile device. Thus, another type of media that may bearthe software elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links or the like, also may be considered as mediabearing the software. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms, such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

What is claimed is:
 1. A computer-implemented method for predictinginformation relating to a vascular lesion of a patient, the methodcomprising: receiving, for each of a plurality of individuals, ageometric model including a plurality of points, biophysical hemodynamiccharacteristics at each of the plurality of points, and an estimate of aprobability of plaque growth, shrinkage, or onset at each of theplurality of points; creating a feature vector comprising thebiophysical hemodynamic characteristics at each of the plurality ofpoints in the geometric model, for each of the plurality of individuals;associating each feature vector with the estimate of the probability ofplaque growth, shrinkage, or onset, for each of the plurality of pointsin the geometric model, for each of the plurality of individuals;receiving, for a patient different from the plurality of individuals, apatient-specific image of the patient's vasculature and biophysicalhemodynamic characteristics at one or more points of the patient'svasculature; segmenting the received patient-specific image based onregions with known biophysical hemodynamic characteristics; generating apatient-specific geometric model of the patient's vasculature using thesegmented patient-specific image, the geometric model including aplurality of points and biophysical hemodynamic characteristics at eachof the plurality of points; creating a feature vector comprising thebiophysical hemodynamic characteristics at each of the plurality ofpoints in the geometric model of the patient's vasculature; comparingthe patient-specific geometric model of the patient's vasculature to thegeometric models of the plurality of individuals, to identify featurevectors common between the patient and each of the plurality ofindividuals; identifying a selected point of a geometric model of aselected individual of the plurality of individuals, and a correspondingselected point in the geometric model of the patient's vasculature,where the differences between the feature vector at the selected pointof the geometric model of the selected individual and the feature vectorat the corresponding selected point in the geometric model of thepatient's vasculature falls below a predetermined threshold; determiningan estimate of a probability of plaque growth, shrinkage, or onset atthe selected point in the geometric model of the patient's vasculature,using the estimate of the probability of plaque growth, shrinkage, oronset associated with the feature vector at the selected point in thegeometric model of the selected individual; generating, using a computerprocessor, a patient-specific prediction or a patient-specificprobability of a development of artery disease for the patient, usingthe determined estimate of the probability of plaque growth, shrinkage,or onset at the selected point in the geometric model of the patient'svasculature; and outputting the patient-specific prediction or apatient-specific probability of a development of artery disease to anelectronic storage medium or display.
 2. The method of claim 1, furthercomprising: receiving physiological or phenotypic parameters associatedwith the patient, wherein the patient-specific prediction or thepatient-specific probability of the development of artery disease forthe patient is further based on the physiological or phenotypicparameters associated with the patient.
 3. The method of claim 1,further comprising: receiving, for the each of plurality of individuals,one or more physiological or phenotypic parameters associated with theindividual, wherein the feature vectors further comprise the one or morephysiological or phenotypic parameters.
 4. The method of claim 1,wherein the estimate of the probability of plaque growth, shrinkage, oronset at each of the plurality of points of the individual's geometricmodel, is associated with the one or more biophysical hemodynamiccharacteristics.
 5. The method of claim 1, wherein the estimates ofprobability of plaque growth, shrinkage, or onset at one of more of theplurality of points are based on one or more physiological or phenotypicparameters and one or more biophysical hemodynamic characteristics ofthe plurality of individuals collected at multiple time points.
 6. Themethod of claim 1, further comprising: updating the one or more of theestimates of probability of plaque growth, shrinkage, or onset at one ormore of the plurality of points of the individuals' geometric modelsbased on the patient-specific image of the patient's vasculature.
 7. Themethod of claim 1, wherein the patient-specific prediction includes apatient-specific prediction of one or more locations of coronarylesions, plaque growth or shrinkage, pathogenesis, or a combinationthereof.
 8. The computer-implemented method of claim 1, wherein thefeature vector further comprises a numerical description of the locationof the point in the geometric model.
 9. The computer-implemented methodof claim 1, wherein the biophysical hemodynamic characteristics includesone or more of: a wall-shear stress value, an oscillatory shear index, aparticle residence time, a Reynolds number, a Womersley number, a localflow rate, a turbulent kinetic energy value, or a hemodynamiccharacteristic obtained from computational fluid dynamics.
 10. A systemfor predicting information relating to a vascular lesion of anindividual, the system comprising: a data storage device storinginstructions for predicting information relating to a coronary lesion;and a processor configured to execute the instructions to perform amethod including: receiving, for each of a plurality of individuals, ageometric model including a plurality of points, biophysical hemodynamiccharacteristics at each of the plurality of points, and an estimate of aprobability of plaque growth, shrinkage, or onset at each of theplurality of points; creating a feature vector comprising of thebiophysical hemodynamic characteristics at each of the plurality ofpoints in the geometric model, for each of the plurality of individuals;associating each feature vector with the estimate of the probability ofplaque growth, shrinkage, or onset, for each of the plurality of pointsin the geometric model, for each of the plurality of individuals;receiving, for a patient different from the plurality of individuals, apatient-specific image of the patient's vasculature and biophysicalhemodynamic characteristics at one or more points of the patient'svasculature; segmenting the received patient-specific image based onregions with known biophysical hemodynamic characteristics; generating apatient-specific geometric model of the patient's vasculature using thesegmented patient-specific image, the geometric model including aplurality of points and biophysical hemodynamic characteristics at eachof the plurality of points; creating a feature vector comprising of thebiophysical hemodynamic characteristics at each of the plurality ofpoints in the geometric model of the patient's vasculature; comparingthe patient-specific geometric model of the patient's vasculature to thegeometric models of the plurality of individuals, to identify featurevectors common between the patient and each of the plurality ofindividuals; identifying a selected point of a geometric model of aselected individual of the plurality of individuals, and a correspondingselected point in the geometric model of the patient's vasculature,where the differences between the feature vector at the selected pointof the geometric model of the selected individual and the feature vectorat the corresponding selected point in the geometric model of thepatient's vasculature falls below a predetermined threshold; determiningan estimate of a probability of plaque growth, shrinkage, or onset atthe selected point in the geometric model of the patient's vasculature,using the estimate of the probability of plaque growth, shrinkage, oronset associated with the feature vector at the selected point in thegeometric model of the selected individual; generating, using a computerprocessor, a patient-specific prediction or a patient-specificprobability of a development of artery disease for the patient, usingthe determined estimate of the probability of plaque growth, shrinkage,or onset at the selected point in the geometric model of the patient'svasculature; and outputting the patient-specific prediction or apatient-specific probability of a development of artery disease to anelectronic storage medium or display.
 11. The system of claim 10,wherein the system is further configured for: receiving physiological orphenotypic parameters associated with the patient, wherein thepatient-specific prediction or the patient-specific probability of thedevelopment of artery disease for the patient is further based on thephysiological or phenotypic parameters associated with the patient. 12.The system of claim 10, wherein the system is further configured for:receiving, for the each of plurality of individuals, one or morephysiological or phenotypic parameters associated with the individual,wherein the feature vectors further comprises the one or morephysiological or phenotypic parameters.
 13. The system of claim 10,wherein the estimate of the probability of plaque growth, shrinkage, oronset at each of the plurality of points of the individual's geometricmodel, is associated with the one or more biophysical hemodynamiccharacteristics.
 14. The system of claim 10, wherein the estimates ofprobability of plaque growth, shrinkage, or onset at one of more of theplurality of points are based on one or more physiological or phenotypicparameters and one or more biophysical hemodynamic characteristics ofthe plurality of individuals collected at multiple time points.
 15. Thesystem of claim 10, wherein the system is further configured for:updating the one or more of the estimates of probability of plaquegrowth, shrinkage, or onset at one or more of the plurality of points ofthe individuals' geometric models based on the patient-specific image ofthe patient's vasculature.
 16. The system of claim 10, wherein thepatient-specific prediction includes a patient-specific prediction ofone or more locations of coronary lesions, plaque growth or shrinkage,pathogenesis, or a combination thereof.
 17. A non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for performing a method ofpredicting information relating to a vascular lesion of an individual,the method comprising: receiving, for each of a plurality ofindividuals, a geometric model including a plurality of points,biophysical hemodynamic characteristics at each of the plurality ofpoints, and an estimate of a probability of plaque growth, shrinkage, oronset at each of the plurality of points; creating a feature vectorcomprising of the biophysical hemodynamic characteristics at each of theplurality of points in the geometric model, for each of the plurality ofindividuals; associating each feature vector with the estimate of theprobability of plaque growth, shrinkage, or onset, for each of theplurality of points in the geometric model, for each of the plurality ofindividuals; receiving, for a patient different from the plurality ofindividuals, a patient-specific image of the patient's vasculature andbiophysical hemodynamic characteristics at one or more points of thepatient's vasculature; segmenting the received patient-specific imagebased on regions with known biophysical hemodynamic characteristics;generating a patient-specific geometric model of the patient'svasculature using the segmented patient-specific image, the geometricmodel including a plurality of points and biophysical hemodynamiccharacteristics at each of the plurality of points; creating a featurevector comprising of the biophysical hemodynamic characteristics at eachof the plurality of points in the geometric model of the patient'svasculature; comparing the patient-specific geometric model of thepatient's vasculature to the geometric models of the plurality ofindividuals, to identify feature vectors common between the patient andeach of the plurality of individuals; identifying a selected point of ageometric model of a selected individual of the plurality ofindividuals, and a corresponding selected point in the geometric modelof the patient's vasculature, where the differences between the featurevector at the selected point of the geometric model of the selectedindividual and the feature vector at the corresponding selected point inthe geometric model of the patient's vasculature falls below apredetermined threshold; determining an estimate of a probability ofplaque growth, shrinkage, or onset at the selected point in thegeometric model of the patient's vasculature, using the estimate of theprobability of plaque growth, shrinkage, or onset associated with thefeature vector at the selected point in the geometric model of theselected individual; generating, using a computer processor, apatient-specific prediction or a patient-specific probability of adevelopment of artery disease for the patient, using the determinedestimate of the probability of plaque growth, shrinkage, or onset at theselected point in the geometric model of the patient's vasculature; andoutputting the patient-specific prediction or a patient-specificprobability of a development of artery disease to an electronic storagemedium or display.
 18. The non-transitory computer readable medium ofclaim 17, further comprising: receiving physiological or phenotypicparameters associated with the patient, wherein the patient-specificprediction or the patient-specific probability of the development ofartery disease for the patient is further based on the physiological orphenotypic parameters associated with the patient.
 19. Thenon-transitory computer readable medium of claim 17, further comprising:receiving, for the each of plurality of individuals, one or morephysiological or phenotypic parameters associated with the individual,wherein the estimate of the probability of plaque growth, shrinkage, oronset at each of the plurality of points of the individual's geometricmodel, is associated with the one or more physiological or phenotypicparameters.
 20. The non-transitory computer readable medium of claim 17,wherein the estimate of the probability of plaque growth, shrinkage, oronset at each of the plurality of points of the individual's geometricmodel, is associated with the one or more biophysical hemodynamiccharacteristics.